Simons Institute for the Theory of Computing

Simons Institute for the Theory of Computing

Views:
6,304,334
Subscribers:
68,700
Videos:
5,454
Duration:
173:07:02:50
United States
United States

Simons Institute for the Theory of Computing is an American YouTube content creator with at least 68.7 thousand subscribers. He published around 5.45 thousand videos which altogether total roughly 6.3 million views.

Created on ● Channel Link: https://www.youtube.com/channel/UCW1C2xOfXsIzPgjXyuhkw9g



Top 100 Most Liked Videos by Simons Institute for the Theory of Computing


Video TitleRatingCategoryGame
1.An observation on Generalization2,052
2.Algorithmic Trading and Machine Learning1,472
3.Nonparametric Bayesian Methods: Models, Algorithms, and Applications I867
4.The Unreasonable Effectiveness of Spectral Graph Theory: A Confluence of Algorithms, Geometry & ...816
5.Variational Inference: Foundations and Innovations814
6.WIT: Women in Theory (of Computer Science) — I Will Survive!731
7.A Tutorial on Reinforcement Learning I675
8.Cryptography: From Mathematical Magic to Secure Communication612
9.Building Human Intelligence at Scale, to Save the Next Generation from ChatGPT564
10.Black Holes and the Quantum-Extended Church-Turing Thesis | Quantum Colloquium549
11.Artificial Stupidity: The New AI and the Future of Fintech532
12.Pairings in Cryptography468
13.Ultraproducts as a Bridge Between Discrete and Continuous Analysis465
14.The Arrow of Time in Causal Networks453
15.Predictive Coding Models of Perception433
16.The Contextual Bandits Problem414
17.Natural Language Understanding: Foundations and State-of-the-Art411
18.The Mathematics of Lattices I407
19.Classical Verification of Quantum Computations394
20.Why Only Us: Language and Evolution383
21.Optimization for Machine Learning I379
22.Nonparametric Bayesian Methods: Models, Algorithms, and Applications II374
23.Polar Codes I372
24.Spectral Graph Theory I: Introduction to Spectral Graph Theory345
25.Optimization I329
26.Spacetime, Entropy, and Quantum Information322
27.Does the Neocortex Use Grid Cell-Like Mechanisms to Learn the Structure of Objects?321
28.High-Dimensional Statistics I317
29.Fully Homomorphic Encryption299
30.Beyond Computation: The P versus NP question (panel discussion)283Discussion
31.On How Machine Learning and Auction Theory Power Facebook Advertising279
32.Algorand's Forthcoming Blockchain Technology278
33.Generalization and Equilibrium in Generative Adversarial Nets (GANs)273
34.Submodularity: Theory and Applications I268
35.Are LLMs the Beginning or End of NLP?261Let's Play
36.Perception as Inference: The Brain and Computation | Theory Shorts254
37.Genome in 3D: Modeling Chromosome Organization251
38.The Mathematics of Causal Inference, with Reflections on Machine Learning and the Logic of Science221
39.On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic216
40.Optimization on Manifolds215
41.The Information Bottleneck Theory of Deep Neural Networks...214
42.Deep Reinforcement Learning212
43.Algorand: The Truly Distributed Ledger210
44.Until the Sun Engulfs the Earth: Lower Bounds in Computational Complexity | Theory Shorts210
45.Dreamcoder: Bootstrapping Inductive Program Synthesis With Wake-Sleep Library Learning204
46.The Entropy Decrement Method and the Erdos Discrepancy Problem203
47.Fully Homomorphic Encryption I202
48.Black Holes, Firewalls, and the Limits of Quantum Computers195
49.Introduction to Cancer Bioinformatics I: Inferring Genomic Variation from Tumor Sequencing Data191
50.Representations for Language: From Word Embeddings to Sentence Meanings188
51.Quantum Algorithms for Hamiltonian Simulation | Quantum Colloquium185
52.Integrating Constraints into Deep Learning Architectures with Structured Layers185
53.Mini Crash Course: Tensor Networks184
54.Dynamic Pricing in Ride-Sharing Platforms183
55.Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to182
56.Natasha 2: Faster Non-convex Optimization Than SGD181
57.Panel Discussion180Discussion
58.Mathematics of Lattices178
59.Bayesian Theories of Perception and Cognition177
60.Tutorial on Deep Learning I177
61.The Prefrontal Cortex as a Meta-Reinforcement Learning System177
62.Introduction to Biological Network Analysis I: Network Basics and Properties177
63.Networks of Spiking Neurons Learn to Learn and Remember174
64.Obviously Strategy-Proof Mechanisms173
65.A Tale of Turing Machines, Quantum-Entangled Particles, and Operator Algebras165
66.Simple, Efficient and Neural Algorithms for Sparse Coding161
67.Scaling Up Bayesian Inference for Big and Complex Data157
68.Unsupervised Representation Learning154
69.Liquid Time Constant Networks150
70.The Science of Cause and Effect: From Deep Learning to Deep Understanding149
71.Nonparametric Bayesian Methods: Models, Algorithms, and Applications IV149
72.Failures of Deep Learning148
73.Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms146
74.Nonparametric Bayesian Methods: Models, Algorithms, and Applications III146
75.Mini Crash Course: Quantum Error Correction146
76.A New Perspective on Adversarial Perturbations141
77.Possible Impossibilities and Impossible Possibilities141
78.Robust Deep Learning Under Distribution Shift137
79.Reinforcement Learning in Recommender Systems: Some Challenges137
80.The Predictive Brain: Michael Pollan, Celeste Kidd, Christos Papadimitriou, and Bruno Olshausen135
81.Real Algebraic Geometry134
82.The Mathematics of Networks132
83.Geometry of Polynomials131
84.Sperm Whale Communication: What we know so far/ Understanding Whale Communication: First steps129
85.Credible Mechanism128
86.Quantum Algorithms for Optimization | Quantum Colloquium128
87.A Theory for Emergence of Complex Skills in Language Models127
88.Tutorial on Differential Privacy126
89.From Classical Statistics to Modern Machine Learning126
90.Breakthroughs — A Refined Laser Method and Faster Matrix Multiplication125
91.Yes, Generative Models Are The New Sparsity124
92.Always Valid Inference: Continuous Monitoring of A/B Tests119
93.Gradient Descent: The Mother of All Algorithms?118
94.Learning and Generalization in Over-parametrized Neural Networks, Going Beyond Kernels117
95.Does Computational Complexity Restrict Artificial Intelligence (AI) and Machine Learning?117
96.Computational Challenges and the Future of ML Panel117
97.Finding Low-Rank Matrices: From Matrix Completion to Recent Trends115
98.Mini Crash Course: Quantum Information Theory115
99.Error error error correcting correcting correcting codes codes codes113
100.Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference113